提出分解变形场的新方法，提升多器官全身CT配准精度
Divide to Conquer: A Field Decomposition Approach for Multi-Organ Whole-Body CT Image Registration
- 将复杂变形场分解为多个器官独立变形分量
- 在691例患者数据上显著优于优化与深度学习基线方法
- 适合需要高精度多器官配准的临床研究场景
图像配准是临床CT图像分析的关键技术。现有方法多针对单一器官，对其他器官性能下降，限制了通用性。多器官配准需同时处理形状、大小和位置各异的多个器官，导致变形场高度复杂，需多层次组合。本研究提出一种新的场分解方法，用于解决多器官全身CT图像配准中的高复杂度变形问题。方法在包含691名患者的纵向数据集上训练与评估，每名患者有两次不同时间点的完整胸腹盆区域扫描。对比两种基线方法：基于优化的方法与基于深度学习的方法。实验结果表明，所提方法在处理多器官全身CT图像复杂变形方面优于基线方法。
原文摘要 · Abstract (English)
Image registration is an essential technique for the analysis of Computed Tomography (CT) images in clinical practice. However, existing methodologies are predominantly tailored to a specific organ of interest and often exhibit lower performance on other organs, thus limiting their generalizability and applicability. Multi-organ registration addresses these limitations, but the simultaneous alignment of multiple organs with diverse shapes, sizes and locations requires a highly complex deformation field with a multi-layer composition of individual deformations. This study introduces a novel field decomposition approach to address the high complexity of deformations in multi-organ whole-body CT image registration. The proposed method is trained and evaluated on a longitudinal dataset of 691 patients, each with two CT images obtained at distinct time points. These scans fully encompass the thoracic, abdominal, and pelvic regions. Two baseline registration methods are selected for this study: one based on optimization techniques and another based on deep learning. Experimental results demonstrate that the proposed approach outperforms baseline methods in handling complex deformations in multi-organ whole-body CT image registration.
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